Indoor wireless telecommunications has been the subject of intense investigation in recent years for both voice and data communication. One particular area of investigation is how to ensure the adequate propagation of a wireless signal from a base station to a wireless terminal through a typical indoor environment. The walls, furniture and other objects in a typical indoor environment scatter the wireless signal and thus produce a complex multi path channel in which the signal propagation characteristics are substantially more difficult to predict than those in outdoor contexts.
Typically, the signal propagation characteristics are considered when designing and installing an indoor wireless telecommunications system. In particular, the signal propagation characteristics are advantageously considered when determining how many base stations are needed to provide coverage for a building and where in the building those base stations should be located. Because base stations are typically expensive to install and operate, it is advantageous to be able to determine how to provide the necessary coverage for the building with the fewest number of base stations. To do this, several techniques have been developed for modeling the propagation of wireless signals indoors.
One technique in the prior art for measuring and modeling indoor signal propagation is adapted from the power-law decay model used in modeling outdoor environments. The power-law decay model assumes that the base station s antenna is high above the ground and that there is line-of-sight propagation to the wireless terminal. In this case, the mean power, P, received at the wireless terminal decays in inverse proportion to the square of the distance from the transmitter, ##EQU3## up to some break-point. Beyond that breakpoint, the mean power at the wireless terminal decays in inverse proportion to the fourth power of the distance from the transmitter: ##EQU4## The location of the break-point is determined by the location at which the ground bounce signal interferes with the line-of-sight signal. For indoor environments, Equation 1 has been adapted to Equation 3, where .gamma. is fit to empirical trial measurements of the building of interest, in well-known fashion: ##EQU5## Unfortunately, Equation 3 typically does not provide a satisfactory model of the building of interest for aiding a designer in designing a wireless system for the building. The use of Equation 3 does, however, have advantages. First, the empirical data needed to fit .gamma. is easily obtainable,, and second the fitting of .gamma. and subsequent use of Equation 3 as a model is computationally straightforward.
H. L. Bertoni, W. Honcharenko, L. R. Maciel, and H. H. Xia, "UHF Propagation Prediction for Wireless Personal Communications, " Proc. of the IEEE, vol. 82, pp. 1333-1359 (September 1994); and J. B. Anderson, T. S. Rappaport, and S. Yoshida, "Propagation Measurements and Models for Wireless Communications Channels, " IEEE Communic. Mag., pp. 42-49 (January 1995) both provide an excellent overview of wireless propagation models in the prior art.
Another technique in the prior art for measuring and modeling indoor RF propagation is adapted from ray-tracing techniques, as taught by S. J. Fortune et al., U.S. Pat. No. 5,450,615, issued Sep. 12, 1995. According to this technique the RF propagation within a building is predicted by modeling an RF signal as a plurality of rays that pass through objects in the building or are reflected off of objects in the building or both. An advantage of this technique is that the resulting wireless propagation model can be very effective for modeling the RF propagation characteristics of the building of interest. There are, however, two disadvantages of this technique. The first disadvantage is that the technique requires an extremely detailed floorplan of the building of interest including the major RF obstacles in the building. For a typical office building, this can be burdensome. The second disadvantage is that the technique is computationally intense, and, therefore, typically requires fast, expensive computers.
Therefore, there exists the need for a wireless propagation model that is effective, that does not require a great deal of empirical data about the building to be gathered and that can be reasonably implemented on a typical desktop computer.